# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_imdb = df_movies.copy()
df_movies_imdb.drop(df_movies_imdb.loc[df_movies_imdb['IMDb'] == "NA"].index, inplace = True)
# df_movies_imdb = df_movies_imdb[df_movies_imdb.IMDb != "NA"]
df_movies_imdb['IMDb'] = df_movies_imdb['IMDb'].astype(int)
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_imdb_movies = df_movies_imdb.loc[df_movies_imdb['Netflix'] == 1]
hulu_imdb_movies = df_movies_imdb.loc[df_movies_imdb['Hulu'] == 1]
prime_video_imdb_movies = df_movies_imdb.loc[df_movies_imdb['Prime Video'] == 1]
disney_imdb_movies = df_movies_imdb.loc[df_movies_imdb['Disney+'] == 1]
df_movies_imdb_group = df_movies_imdb.copy()
plt.figure(figsize = (10, 10))
corr = df_movies_imdb.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_imdb_high_movies = df_movies_imdb.sort_values(by = 'IMDb', ascending = False).reset_index()
df_imdb_high_movies = df_imdb_high_movies.drop(['index'], axis = 1)
# filter = (df_movies_imdb['IMDb'] == (df_movies_imdb['IMDb'].max()))
# df_imdb_high_movies = df_movies_imdb[filter]
# highest_rated_movies = df_movies_imdb.loc[df_movies_imdb['IMDb'].idxmax()]
print('\nMovies with Highest Ever IMDb are : \n')
df_imdb_high_movies.head(5)
Movies with Highest Ever IMDb are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7767 | Operation Toussaint: Operation Underground Rai... | 2018 | NR | 9 | NA | Nick Nanton,Ramy Romany | Tim Ballard,Anthony Robbins,Glenn Beck,Orrin H... | Documentary | United States | English | A comedy about a cheating husband (Ray Liotta)... | 82 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 1 | 7300 | Finding Family | 2013 | 13 | 9 | NA | Chris Leslie,Oggi Tomic | NA | Documentary,Family,History,War | United Kingdom,Bosnia and Herzegovina | Bosnian,English | A woman named Grace retires with her two child... | 56 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 2 | 13378 | Concrete Cowboys | 1979 | NR | 9 | 79 | Ty Javos | Ty Javos,Benjamin Davies,Chris Sunberg,Bryce S... | Short,Drama | Canada | English | Rachel is 8 and for the first time on Christma... | 5 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 3 | 8678 | Weaving the Past: Journey of Discovery | 2014 | NR | 9 | NA | Walter Dominguez | NA | Documentary | United States | Spanish,English | Sonny "Sundown" Garcia is the top North Americ... | 126 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 4 | 16587 | Almost Impossible | 2017 | 18 | 9 | NA | Andrew Espinoza Long | Erica Chase,Andrew Espinoza Long,Richard Randa... | Short,Drama | United States | English | NA | 5 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
fig = px.bar(y = df_imdb_high_movies['Title'][:15],
x = df_imdb_high_movies['IMDb'][:15],
color = df_imdb_high_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Highest IMDb : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_imdb_low_movies = df_movies_imdb.sort_values(by = 'IMDb', ascending = True).reset_index()
df_imdb_low_movies = df_imdb_low_movies.drop(['index'], axis = 1)
# filter = (df_movies_imdb['IMDb'] == (df_movies_imdb['IMDb'].min()))
# df_imdb_low_movies = df_movies_imdb[filter]
print('\nMovies with Lowest Ever IMDb are : \n')
df_imdb_low_movies.head(5)
Movies with Lowest Ever IMDb are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13433 | In Memoriam Alexander Litvinenko | 2007 | NR | 0 | NA | Jos de Putter | NA | Documentary | Netherlands | English | The Hellcats are an all-female gang bent on bu... | 55 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 1 | 13441 | From Philadelphia To Fallujah | 2011 | NR | 0 | NA | David Hammelburg | Harry Lennix | Documentary,Short | United States | English | Bee People is not just a documentary. It is an... | 42 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 2 | 4238 | 9/11: 15 years later | 2016 | NR | 0 | NA | NA | Richard Gage,Luke Rudkowski,Coen Vermeeren | Documentary | Netherlands | English | NA | 60 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 3 | 13549 | Return of the Boogeyman | 1994 | NR | 1 | NA | Deland Nuse,Ulli Lommel | Kelly Galindo,Suzanna Love,Omar Kaczmarczyk,Ma... | Horror | United States | English | NA | 76 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 4 | 15286 | Curse of Bigfoot | 1978 | NR | 1 | NA | Dave Flocker | Bob Clymire,Jan Swihart,Bill Simonsen,Dennis K... | Horror | United States | English | A headstrong animal-rights activist group plan... | 88 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = df_imdb_low_movies['Title'][:15],
x = df_imdb_low_movies['IMDb'][:15],
color = df_imdb_low_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Lowest IMDb : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_imdb['IMDb'].unique().shape[0]}' unique IMDb s were Given, They were Like this,\n
{df_movies_imdb.sort_values(by = 'IMDb', ascending = False)['IMDb'].unique()}\n
The Highest Ever IMDb Ever Any Movie Got is '{df_imdb_high_movies['Title'][0]}' : '{df_imdb_high_movies['IMDb'].max()}'\n
The Lowest Ever IMDb Ever Any Movie Got is '{df_imdb_low_movies['Title'][0]}' : '{df_imdb_low_movies['IMDb'].min()}'\n
''')
Total '10' unique IMDb s were Given, They were Like this,
[9 8 7 6 5 4 3 2 1 0]
The Highest Ever IMDb Ever Any Movie Got is 'Operation Toussaint: Operation Underground Railroad and the Fight to End Modern Day Slavery' : '9'
The Lowest Ever IMDb Ever Any Movie Got is 'In Memoriam Alexander Litvinenko' : '0'
netflix_imdb_high_movies = df_imdb_high_movies.loc[df_imdb_high_movies['Netflix']==1].reset_index()
netflix_imdb_high_movies = netflix_imdb_high_movies.drop(['index'], axis = 1)
netflix_imdb_low_movies = df_imdb_low_movies.loc[df_imdb_low_movies['Netflix']==1].reset_index()
netflix_imdb_low_movies = netflix_imdb_low_movies.drop(['index'], axis = 1)
netflix_imdb_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 812 | God of War | 2017 | NR | 9 | 46 | Cory Barlog | Christopher Judge,Sunny Suljic,Jeremy Davies,D... | Action,Adventure,Drama,Fantasy | United States | English,Norse, Old | Kratos, the God of War, has defeated the Gods ... | NA | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 931 | Natsamrat | 2016 | NR | 9 | NA | Mahesh Manjrekar | Nana Patekar,Medha Manjrekar,Mrunmayee Deshpan... | Drama,Family | India | Marathi | The film is a tragedy about a veteran theatre ... | 166 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 1101 | It Takes Two | 1995 | 7 | 9 | 8 | Josef Fares | Joseph Balderrama,Annabelle Dowler,Clare Corbe... | Adventure | Sweden | English | Embark on the craziest journey of your life in... | 101 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
| 3 | 1026 | Bo Burnham: What. | 2013 | 18 | 8 | NA | Bo Burnham,Christopher Storer | Bo Burnham | Comedy,Music | United States | English | NA | 60 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 1046 | Struggle: The Life and Lost Art of Szukalski | 2018 | 18 | 8 | NA | Irek Dobrowolski | Stanislav Szukalski,Glenn Bray,Robert Williams... | Documentary | Poland,United States | English | In old Betamax footage, the Polish-American ar... | 115 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
fig = px.bar(y = netflix_imdb_high_movies['Title'][:15],
x = netflix_imdb_high_movies['IMDb'][:15],
color = netflix_imdb_high_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Highest IMDb : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_imdb_low_movies['Title'][:15],
x = netflix_imdb_low_movies['IMDb'][:15],
color = netflix_imdb_low_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Lowest IMDb : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_imdb_high_movies = df_imdb_high_movies.loc[df_imdb_high_movies['Hulu']==1].reset_index()
hulu_imdb_high_movies = hulu_imdb_high_movies.drop(['index'], axis = 1)
hulu_imdb_low_movies = df_imdb_low_movies.loc[df_imdb_low_movies['Hulu']==1].reset_index()
hulu_imdb_low_movies = hulu_imdb_low_movies.drop(['index'], axis = 1)
hulu_imdb_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16587 | Almost Impossible | 2017 | 18 | 9 | NA | Andrew Espinoza Long | Erica Chase,Andrew Espinoza Long,Richard Randa... | Short,Drama | United States | English | NA | 5 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 1 | 16592 | The Paley Center | 2000 | NR | 9 | NA | Brad Lachman | Margaret Cho,Raúl Esparza,Kelli Giddish,Marisk... | NA | United States | English | NA | NA | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 2 | 3460 | The Dark Knight | 2008 | 13 | 9 | 87 | Christopher Nolan | Christian Bale,Heath Ledger,Aaron Eckhart,Mich... | Action,Crime,Drama,Thriller | United States,United Kingdom | English,Mandarin | Set within a year after the events of Batman B... | 152 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 3 | 3490 | Apollo 11 | 2019 | 0 | 8 | 99 | Todd Douglas Miller | Neil Armstrong,Michael Collins,Buzz Aldrin,Dek... | Documentary,History | United States | English | On its fiftieth anniversary, the events surrou... | 93 | movie | 0 | 1 | 0 | 0 | 0 | Hulu |
| 4 | 3480 | Free Solo | 2018 | 13 | 8 | 97 | Jimmy Chin,Elizabeth Chai Vasarhelyi | Alex Honnold,Tommy Caldwell,Jimmy Chin,Cheyne ... | Documentary,Adventure,Sport | United States | English | NA | 100 | movie | 0 | 1 | 0 | 1 | 0 | Disney+ |
fig = px.bar(y = hulu_imdb_high_movies['Title'][:15],
x = hulu_imdb_high_movies['IMDb'][:15],
color = hulu_imdb_high_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Highest IMDb : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_imdb_low_movies['Title'][:15],
x = hulu_imdb_low_movies['IMDb'][:15],
color = hulu_imdb_low_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Lowest IMDb : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_imdb_high_movies = df_imdb_high_movies.loc[df_imdb_high_movies['Prime Video']==1].reset_index()
prime_video_imdb_high_movies = prime_video_imdb_high_movies.drop(['index'], axis = 1)
prime_video_imdb_low_movies = df_imdb_low_movies.loc[df_imdb_low_movies['Prime Video']==1].reset_index()
prime_video_imdb_low_movies = prime_video_imdb_low_movies.drop(['index'], axis = 1)
prime_video_imdb_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7767 | Operation Toussaint: Operation Underground Rai... | 2018 | NR | 9 | NA | Nick Nanton,Ramy Romany | Tim Ballard,Anthony Robbins,Glenn Beck,Orrin H... | Documentary | United States | English | A comedy about a cheating husband (Ray Liotta)... | 82 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 1 | 7300 | Finding Family | 2013 | 13 | 9 | NA | Chris Leslie,Oggi Tomic | NA | Documentary,Family,History,War | United Kingdom,Bosnia and Herzegovina | Bosnian,English | A woman named Grace retires with her two child... | 56 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 2 | 13378 | Concrete Cowboys | 1979 | NR | 9 | 79 | Ty Javos | Ty Javos,Benjamin Davies,Chris Sunberg,Bryce S... | Short,Drama | Canada | English | Rachel is 8 and for the first time on Christma... | 5 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 3 | 8678 | Weaving the Past: Journey of Discovery | 2014 | NR | 9 | NA | Walter Dominguez | NA | Documentary | United States | Spanish,English | Sonny "Sundown" Garcia is the top North Americ... | 126 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
| 4 | 6823 | Escape from Firebase Kate | 2015 | NR | 9 | NA | Paul Kakert | J.V. Martin | Documentary | United States | English | This is a thriller story about how police work... | 60 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video |
fig = px.bar(y = prime_video_imdb_high_movies['Title'][:15],
x = prime_video_imdb_high_movies['IMDb'][:15],
color = prime_video_imdb_high_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Highest IMDb : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_imdb_low_movies['Title'][:15],
x = prime_video_imdb_low_movies['IMDb'][:15],
color = prime_video_imdb_low_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Lowest IMDb : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_imdb_high_movies = df_imdb_high_movies.loc[df_imdb_high_movies['Disney+']==1].reset_index()
disney_imdb_high_movies = disney_imdb_high_movies.drop(['index'], axis = 1)
disney_imdb_low_movies = df_imdb_low_movies.loc[df_imdb_low_movies['Disney+']==1].reset_index()
disney_imdb_low_movies = disney_imdb_low_movies.drop(['index'], axis = 1)
disney_imdb_high_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16912 | Marvel's Spider-Man | 2017 | 7 | 9 | NA | Ryan Smith | Yuri Lowenthal,Tara Platt,Travis Willingham,Wi... | Action,Adventure,Comedy,Fantasy,Mystery,Sci-Fi | United States | English | Hercules, son of the Greek God, Zeus, is turne... | NA | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 1 | 5254 | Empire of Dreams: The Story of the Star Wars T... | 2004 | 13 | 8 | NA | Edith Becker,Kevin Burns | Robert Clotworthy,Walter Cronkite,George Lucas... | Documentary,History,Sci-Fi | United States | English | Nicko and his brother take off from Canada in ... | 151 | movie | 0 | 0 | 1 | 1 | 0 | Prime Video |
| 2 | 15788 | The Straight Story | 1999 | 0 | 8 | 95 | David Lynch | Sissy Spacek,Jane Galloway Heitz,Joseph A. Car... | Biography,Drama | France,United Kingdom,United States | English | Four children from the same family have to lea... | 112 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 3 | 15780 | Togo | 2019 | 7 | 8 | 92 | Ericson Core | Willem Dafoe,Julianne Nicholson,Christopher He... | Adventure,Biography,Drama,Family,History | United States | English | Jedi Master-in-hiding Luke Skywalker unwilling... | 113 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ |
| 4 | 3480 | Free Solo | 2018 | 13 | 8 | 97 | Jimmy Chin,Elizabeth Chai Vasarhelyi | Alex Honnold,Tommy Caldwell,Jimmy Chin,Cheyne ... | Documentary,Adventure,Sport | United States | English | NA | 100 | movie | 0 | 1 | 0 | 1 | 0 | Disney+ |
fig = px.bar(y = disney_imdb_high_movies['Title'][:15],
x = disney_imdb_high_movies['IMDb'][:15],
color = disney_imdb_high_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Highest IMDb : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_imdb_low_movies['Title'][:15],
x = disney_imdb_low_movies['IMDb'][:15],
color = disney_imdb_low_movies['IMDb'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'IMDb : Rating'},
title = 'Movies with Lowest IMDb : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The Movie with Highest IMDb Ever Got is '{df_imdb_high_movies['Title'][0]}' : '{df_imdb_high_movies['IMDb'].max()}'\n
The Movie with Lowest IMDb Ever Got is '{df_imdb_low_movies['Title'][0]}' : '{df_imdb_low_movies['IMDb'].min()}'\n
The Movie with Highest IMDb on 'Netflix' is '{netflix_imdb_high_movies['Title'][0]}' : '{netflix_imdb_high_movies['IMDb'].max()}'\n
The Movie with Lowest IMDb on 'Netflix' is '{netflix_imdb_low_movies['Title'][0]}' : '{netflix_imdb_low_movies['IMDb'].min()}'\n
The Movie with Highest IMDb on 'Hulu' is '{hulu_imdb_high_movies['Title'][0]}' : '{hulu_imdb_high_movies['IMDb'].max()}'\n
The Movie with Lowest IMDb on 'Hulu' is '{hulu_imdb_low_movies['Title'][0]}' : '{hulu_imdb_low_movies['IMDb'].min()}'\n
The Movie with Highest IMDb on 'Prime Video' is '{prime_video_imdb_high_movies['Title'][0]}' : '{prime_video_imdb_high_movies['IMDb'].max()}'\n
The Movie with Lowest IMDb on 'Prime Video' is '{prime_video_imdb_low_movies['Title'][0]}' : '{prime_video_imdb_low_movies['IMDb'].min()}'\n
The Movie with Highest IMDb on 'Disney+' is '{disney_imdb_high_movies['Title'][0]}' : '{disney_imdb_high_movies['IMDb'].max()}'\n
The Movie with Lowest IMDb on 'Disney+' is '{disney_imdb_low_movies['Title'][0]}' : '{disney_imdb_low_movies['IMDb'].min()}'\n
''')
The Movie with Highest IMDb Ever Got is 'Operation Toussaint: Operation Underground Railroad and the Fight to End Modern Day Slavery' : '9'
The Movie with Lowest IMDb Ever Got is 'In Memoriam Alexander Litvinenko' : '0'
The Movie with Highest IMDb on 'Netflix' is 'God of War' : '9'
The Movie with Lowest IMDb on 'Netflix' is 'Aerials' : '1'
The Movie with Highest IMDb on 'Hulu' is 'Almost Impossible' : '9'
The Movie with Lowest IMDb on 'Hulu' is '9/11: 15 years later' : '0'
The Movie with Highest IMDb on 'Prime Video' is 'Operation Toussaint: Operation Underground Railroad and the Fight to End Modern Day Slavery' : '9'
The Movie with Lowest IMDb on 'Prime Video' is 'In Memoriam Alexander Litvinenko' : '0'
The Movie with Highest IMDb on 'Disney+' is 'Marvel's Spider-Man' : '9'
The Movie with Lowest IMDb on 'Disney+' is 'Hacksaw' : '1'
print(f'''
Accross All Platforms the Average IMDb is '{round(df_movies_imdb['IMDb'].mean(), ndigits = 2)}'\n
The Average IMDb on 'Netflix' is '{round(netflix_imdb_movies['IMDb'].mean(), ndigits = 2)}'\n
The Average IMDb on 'Hulu' is '{round(hulu_imdb_movies['IMDb'].mean(), ndigits = 2)}'\n
The Average IMDb on 'Prime Video' is '{round(prime_video_imdb_movies['IMDb'].mean(), ndigits = 2)}'\n
The Average IMDb on 'Disney+' is '{round(disney_imdb_movies['IMDb'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average IMDb is '5.53'
The Average IMDb on 'Netflix' is '5.83'
The Average IMDb on 'Hulu' is '5.83'
The Average IMDb on 'Prime Video' is '5.41'
The Average IMDb on 'Disney+' is '6.0'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_imdb['IMDb'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_imdb['IMDb'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('IMDb s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_imdb_movies['IMDb'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_imdb_movies['IMDb'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_imdb_movies['IMDb'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_imdb_movies['IMDb'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
def round_val(data):
if str(data) != 'nan':
return round(data)
df_movies_imdb_group['IMDb Group'] = df_movies_imdb['IMDb'].apply(round_val)
imdb_values = df_movies_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).tolist()
imdb_index = df_movies_imdb_group['IMDb Group'].value_counts().sort_index(ascending = False).index
# imdb_values, imdb_index
imdb_group_count = df_movies_imdb_group.groupby('IMDb Group')['Title'].count()
imdb_group_movies = df_movies_imdb_group.groupby('IMDb Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
imdb_group_data_movies = pd.concat([imdb_group_count, imdb_group_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
imdb_group_data_movies = imdb_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
# IMDb Group with Movies Counts - All Platforms Combined
imdb_group_data_movies.sort_values(by = 'Movies Count', ascending = False)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 6 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 5 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 7 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 4 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 3 | 3 | 982 | 82 | 22 | 893 | 4 |
| 8 | 8 | 652 | 171 | 49 | 433 | 31 |
| 2 | 2 | 392 | 33 | 7 | 359 | 1 |
| 1 | 1 | 44 | 2 | 0 | 40 | 2 |
| 9 | 9 | 22 | 3 | 3 | 16 | 1 |
| 0 | 0 | 3 | 0 | 1 | 2 | 0 |
imdb_group_data_movies.sort_values(by = 'IMDb Group', ascending = False)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 9 | 9 | 22 | 3 | 3 | 16 | 1 |
| 8 | 8 | 652 | 171 | 49 | 433 | 31 |
| 7 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 6 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 5 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 4 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 3 | 3 | 982 | 82 | 22 | 893 | 4 |
| 2 | 2 | 392 | 33 | 7 | 359 | 1 |
| 1 | 1 | 44 | 2 | 0 | 40 | 2 |
| 0 | 0 | 3 | 0 | 1 | 2 | 0 |
fig = px.bar(y = imdb_group_data_movies['Movies Count'],
x = imdb_group_data_movies['IMDb Group'],
color = imdb_group_data_movies['IMDb Group'],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies Count', 'x' : 'IMDb : Rating'},
title = 'Movies with Group IMDb : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
fig = px.pie(imdb_group_data_movies[:10],
names = imdb_group_data_movies['IMDb Group'],
values = imdb_group_data_movies['Movies Count'],
color = imdb_group_data_movies['Movies Count'],
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textinfo = 'percent+label',
title = 'Movies Count based on IMDb Group')
fig.show()
df_imdb_group_high_movies = imdb_group_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_imdb_group_high_movies = df_imdb_group_high_movies.drop(['index'], axis = 1)
# filter = (imdb_group_data_movies['Movies Count'] == (imdb_group_data_movies['Movies Count'].max()))
# df_imdb_group_high_movies = imdb_group_data_movies[filter]
# highest_rated_movies = imdb_group_data_movies.loc[imdb_group_data_movies['Movies Count'].idxmax()]
# print('\nIMDb with Highest Ever Movies Count are : All Platforms Combined\n')
df_imdb_group_high_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 1 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 2 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 3 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 4 | 3 | 982 | 82 | 22 | 893 | 4 |
df_imdb_group_low_movies = imdb_group_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_imdb_group_low_movies = df_imdb_group_low_movies.drop(['index'], axis = 1)
# filter = (imdb_group_data_movies['Movies Count'] = = (imdb_group_data_movies['Movies Count'].min()))
# df_imdb_group_low_movies = imdb_group_data_movies[filter]
# print('\nIMDb with Lowest Ever Movies Count are : All Platforms Combined\n')
df_imdb_group_low_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 0 | 3 | 0 | 1 | 2 | 0 |
| 1 | 9 | 22 | 3 | 3 | 16 | 1 |
| 2 | 1 | 44 | 2 | 0 | 40 | 2 |
| 3 | 2 | 392 | 33 | 7 | 359 | 1 |
| 4 | 8 | 652 | 171 | 49 | 433 | 31 |
print(f'''
Total '{df_movies_imdb['IMDb'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{imdb_group_data_movies['IMDb Group'].unique().shape[0]}' IMDb Group, They were Like this, \n
{imdb_group_data_movies.sort_values(by = 'Movies Count', ascending = False)['IMDb Group'].unique()} etc. \n
The IMDb Group with Highest Movies Count have '{imdb_group_data_movies['Movies Count'].max()}' Movies Available is '{df_imdb_group_high_movies['IMDb Group'][0]}', &\n
The IMDb Group with Lowest Movies Count have '{imdb_group_data_movies['Movies Count'].min()}' Movies Available is '{df_imdb_group_low_movies['IMDb Group'][0]}'
''')
Total '16595' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '10' IMDb Group, They were Like this,
[6 5 7 4 3 8 2 1 9 0] etc.
The IMDb Group with Highest Movies Count have '5185' Movies Available is '6', &
The IMDb Group with Lowest Movies Count have '3' Movies Available is '0'
netflix_imdb_group_movies = imdb_group_data_movies[imdb_group_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_movies = netflix_imdb_group_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_imdb_group_high_movies = df_imdb_group_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_imdb_group_high_movies = netflix_imdb_group_high_movies.drop(['index'], axis = 1)
netflix_imdb_group_low_movies = df_imdb_group_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_imdb_group_low_movies = netflix_imdb_group_low_movies.drop(['index'], axis = 1)
netflix_imdb_group_high_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 1 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 2 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 3 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 4 | 8 | 652 | 171 | 49 | 433 | 31 |
hulu_imdb_group_movies = imdb_group_data_movies[imdb_group_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_movies = hulu_imdb_group_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_imdb_group_high_movies = df_imdb_group_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_imdb_group_high_movies = hulu_imdb_group_high_movies.drop(['index'], axis = 1)
hulu_imdb_group_low_movies = df_imdb_group_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_imdb_group_low_movies = hulu_imdb_group_low_movies.drop(['index'], axis = 1)
hulu_imdb_group_high_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 1 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 2 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 3 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 4 | 8 | 652 | 171 | 49 | 433 | 31 |
prime_video_imdb_group_movies = imdb_group_data_movies[imdb_group_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_movies = prime_video_imdb_group_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_imdb_group_high_movies = df_imdb_group_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_imdb_group_high_movies = prime_video_imdb_group_high_movies.drop(['index'], axis = 1)
prime_video_imdb_group_low_movies = df_imdb_group_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_imdb_group_low_movies = prime_video_imdb_group_low_movies.drop(['index'], axis = 1)
prime_video_imdb_group_high_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 1 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 2 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 3 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 4 | 3 | 982 | 82 | 22 | 893 | 4 |
disney_imdb_group_movies = imdb_group_data_movies[imdb_group_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_movies = disney_imdb_group_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_imdb_group_high_movies = df_imdb_group_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_imdb_group_high_movies = disney_imdb_group_high_movies.drop(['index'], axis = 1)
disney_imdb_group_low_movies = df_imdb_group_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_imdb_group_low_movies = disney_imdb_group_low_movies.drop(['index'], axis = 1)
disney_imdb_group_high_movies.head(5)
| IMDb Group | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 6 | 5185 | 1238 | 346 | 3607 | 225 |
| 1 | 7 | 3463 | 909 | 268 | 2290 | 145 |
| 2 | 5 | 3837 | 823 | 251 | 2795 | 117 |
| 3 | 4 | 2015 | 321 | 102 | 1608 | 34 |
| 4 | 8 | 652 | 171 | 49 | 433 | 31 |
print(f'''
The IMDb Group with Highest Movies Count Ever Got is '{df_imdb_group_high_movies['IMDb Group'][0]}' : '{df_imdb_group_high_movies['Movies Count'].max()}'\n
The IMDb Group with Lowest Movies Count Ever Got is '{df_imdb_group_low_movies['IMDb Group'][0]}' : '{df_imdb_group_low_movies['Movies Count'].min()}'\n
The IMDb Group with Highest Movies Count on 'Netflix' is '{netflix_imdb_group_high_movies['IMDb Group'][0]}' : '{netflix_imdb_group_high_movies['Netflix'].max()}'\n
The IMDb Group with Lowest Movies Count on 'Netflix' is '{netflix_imdb_group_low_movies['IMDb Group'][0]}' : '{netflix_imdb_group_low_movies['Netflix'].min()}'\n
The IMDb Group with Highest Movies Count on 'Hulu' is '{hulu_imdb_group_high_movies['IMDb Group'][0]}' : '{hulu_imdb_group_high_movies['Hulu'].max()}'\n
The IMDb Group with Lowest Movies Count on 'Hulu' is '{hulu_imdb_group_low_movies['IMDb Group'][0]}' : '{hulu_imdb_group_low_movies['Hulu'].min()}'\n
The IMDb Group with Highest Movies Count on 'Prime Video' is '{prime_video_imdb_group_high_movies['IMDb Group'][0]}' : '{prime_video_imdb_group_high_movies['Prime Video'].max()}'\n
The IMDb Group with Lowest Movies Count on 'Prime Video' is '{prime_video_imdb_group_low_movies['IMDb Group'][0]}' : '{prime_video_imdb_group_low_movies['Prime Video'].min()}'\n
The IMDb Group with Highest Movies Count on 'Disney+' is '{disney_imdb_group_high_movies['IMDb Group'][0]}' : '{disney_imdb_group_high_movies['Disney+'].max()}'\n
The IMDb Group with Lowest Movies Count on 'Disney+' is '{disney_imdb_group_low_movies['IMDb Group'][0]}' : '{disney_imdb_group_low_movies['Disney+'].min()}'\n
''')
The IMDb Group with Highest Movies Count Ever Got is '6' : '5185'
The IMDb Group with Lowest Movies Count Ever Got is '0' : '3'
The IMDb Group with Highest Movies Count on 'Netflix' is '6' : '1238'
The IMDb Group with Lowest Movies Count on 'Netflix' is '0' : '0'
The IMDb Group with Highest Movies Count on 'Hulu' is '6' : '346'
The IMDb Group with Lowest Movies Count on 'Hulu' is '1' : '0'
The IMDb Group with Highest Movies Count on 'Prime Video' is '6' : '3607'
The IMDb Group with Lowest Movies Count on 'Prime Video' is '0' : '2'
The IMDb Group with Highest Movies Count on 'Disney+' is '6' : '225'
The IMDb Group with Lowest Movies Count on 'Disney+' is '0' : '0'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_i_ax1 = sns.barplot(x = netflix_imdb_group_movies['IMDb Group'][:10], y = netflix_imdb_group_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = hulu_imdb_group_movies['IMDb Group'][:10], y = hulu_imdb_group_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = prime_video_imdb_group_movies['IMDb Group'][:10], y = prime_video_imdb_group_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = disney_imdb_group_movies['IMDb Group'][:10], y = disney_imdb_group_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
plt.show()
plt.figure(figsize = (20, 5))
sns.lineplot(x = imdb_group_data_movies['IMDb Group'], y = imdb_group_data_movies['Netflix'], color = 'red')
sns.lineplot(x = imdb_group_data_movies['IMDb Group'], y = imdb_group_data_movies['Hulu'], color = 'lightgreen')
sns.lineplot(x = imdb_group_data_movies['IMDb Group'], y = imdb_group_data_movies['Prime Video'], color = 'lightblue')
sns.lineplot(x = imdb_group_data_movies['IMDb Group'], y = imdb_group_data_movies['Disney+'], color = 'darkblue')
plt.xlabel('IMDb Group', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
print(f'''
Accross All Platforms Total Count of IMDb Group is '{imdb_group_data_movies['IMDb Group'].unique().shape[0]}'\n
Total Count of IMDb Group on 'Netflix' is '{netflix_imdb_group_movies['IMDb Group'].unique().shape[0]}'\n
Total Count of IMDb Group on 'Hulu' is '{hulu_imdb_group_movies['IMDb Group'].unique().shape[0]}'\n
Total Count of IMDb Group on 'Prime Video' is '{prime_video_imdb_group_movies['IMDb Group'].unique().shape[0]}'\n
Total Count of IMDb Group on 'Disney+' is '{disney_imdb_group_movies['IMDb Group'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of IMDb Group is '10'
Total Count of IMDb Group on 'Netflix' is '9'
Total Count of IMDb Group on 'Hulu' is '9'
Total Count of IMDb Group on 'Prime Video' is '10'
Total Count of IMDb Group on 'Disney+' is '9'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_i_ax1 = sns.lineplot(y = imdb_group_data_movies['IMDb Group'], x = imdb_group_data_movies['Netflix'], color = 'red', ax = axes[0, 0])
h_i_ax2 = sns.lineplot(y = imdb_group_data_movies['IMDb Group'], x = imdb_group_data_movies['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_i_ax3 = sns.lineplot(y = imdb_group_data_movies['IMDb Group'], x = imdb_group_data_movies['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_i_ax4 = sns.lineplot(y = imdb_group_data_movies['IMDb Group'], x = imdb_group_data_movies['Disney+'], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_i_ax1 = sns.barplot(x = imdb_group_data_movies['IMDb Group'][:10], y = imdb_group_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_i_ax2 = sns.barplot(x = imdb_group_data_movies['IMDb Group'][:10], y = imdb_group_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_i_ax3 = sns.barplot(x = imdb_group_data_movies['IMDb Group'][:10], y = imdb_group_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_i_ax4 = sns.barplot(x = imdb_group_data_movies['IMDb Group'][:10], y = imdb_group_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_i_ax1.title.set_text(labels[0])
h_i_ax2.title.set_text(labels[1])
p_i_ax3.title.set_text(labels[2])
d_i_ax4.title.set_text(labels[3])
plt.show()